Data Engineer – Business Intelligence & Dashboards
Muss:PythonAWSCloudData
Roles & Responsibilities
- Engage with business units to understand data, reporting, KPI, and analytics requirements .
- Facilitate requirements workshops and translate business needs into clear data specifications.
- Act as the liaison between business stakeholders and the technical data platform team.
- Design and develop data transformation logic for reporting and analytics datasets.
- Build and maintain data models in Amazon Redshift or the curated/consumption zone of the Data Lake.
- Ensure reporting datasets are accurate, consistent, and complete through data quality checks and validation.
- Design, develop, and maintain interactive Tableau dashboards and reports using Amazon Redshift or S3.
- Develop near-real-time dashboards, ad-hoc analysis views, and scheduled reports based on business needs.
- Improve dashboard usability based on stakeholder feedback and evolving requirements.
- Maintain data definitions, business glossary terms, dashboard logic, data sources, transformation logic, and report specifications in the AWS Glue Data Catalogue.
- Support self-service analytics by enabling business users to discover and access data independently.
Requirements
Essential
- Minimum 3 years of experience in data engineering, business intelligence, or data analytics.
- Strong SQL skills for querying, transformation, and data modelling.
- Hands-on experience with Tableau or equivalent BI tools such as Power BI or QuickSight.
- Experience with cloud data platforms, preferably AWS, Amazon Redshift, S3, and AWS Glue .
- Strong stakeholder engagement and communication skills with the ability to translate business requirements into technical specifications.
- Experience with analytical data modelling , including star schema and dimensional modelling.
Preferred
- Familiarity with Python for data transformation and automation.
- Experience with AWS Glue Data Catalogue or similar metadata management tools.
- Exposure to Salesforce or other SaaS data sources .
- Experience in a government or public sector environment .
- Understanding of data governance and data quality principles .